Introduction

Claude Skills is a powerful feature introduced by Anthropic that enables Claude to perform specialized tasks by connecting to external tools and services. This capability significantly expands Claude’s functionality beyond its core language model capabilities, allowing it to interact with the outside world, access real-time information, and perform actions on behalf of users.

This technical blog explores the architecture, workflow, and integration possibilities of Claude Skills, with a particular focus on how they relate to other agent frameworks like A2A and MCP server.

What are Claude Skills?

A Skill gives a large model a specific capability, similar to the concept of MCP (Model Context Protocol), which was also proposed by Anthropic.

For example, if you want to check tomorrow’s weather, the model itself can’t do that directly, it needs to connect to the Internet to search for weather forecasts. Previously, this was possible through MCP. Now, the same can be achieved using a Skill, e.g., a “weather” Skill might be a Python method that queries a weather website for data.

Skill Structure

In Claude’s settings, there are some built-in Skills. A Skill is a set of instructions stored in a folder, containing:

  • A file named SKILL.md (in Markdown format, not JSON)
  • Other files such as Python scripts

When the AI agent tries to solve a specific problem, it refers to these files.

SKILL.md must start with YAML frontmatter that includes at least two fields:

  • name: The name of the skill
  • description: What the skill does

When the agent starts, it preloads these metadata items into its system prompt so Claude knows what each installed Skill does.

Core Capabilities

Claude Skills are specialized capabilities that extend Claude’s functionality by allowing it to:

  1. Connect to external APIs and services
  2. Access real-time information beyond its training data
  3. Perform actions in the digital world on behalf of users
  4. Process and analyze data from various sources
  5. Integrate with existing tools and workflows
  6. Define repeatable workflows with explicit tool usage instructions

Skills transform Claude from a conversational AI into a more capable agent that can interact with external systems while maintaining its core strengths in understanding, reasoning, and communication.

Architecture of Claude Skills

The architecture of Claude Skills is designed to be modular, secure, and extensible. Based on the latest information, here’s a more accurate representation of how Claude Skills are structured:

graph TB
    User[User] --> Claude[Claude AI]
    Claude --> SkillsRouter[Skills Router]
    
    subgraph "Claude Skills System"
        SkillsRouter --> SkillRegistry[Skill Registry]
        SkillsRouter --> ExecutionEngine[Execution Engine]
        SkillsRouter --> SecurityManager[Security Manager]
        SkillRegistry --> Skill1[Skill 1]
        SkillRegistry --> Skill2[Skill 2]
        SkillRegistry --> SkillN[Skill N]
    end
    
    ExecutionEngine --> ExternalAPI1[External API 1]
    ExecutionEngine --> ExternalAPI2[External API 2]
    ExecutionEngine --> ExternalAPIN[External API N]
    
    SecurityManager --> AuthenticationLayer[Authentication Layer]
    SecurityManager --> AuthorizationLayer[Authorization Layer]
    
    ExternalAPI1 --> ExternalService1[External Service 1]
    ExternalAPI2 --> ExternalService2[External Service 2]
    ExternalAPIN --> ExternalServiceN[External Service N]
    
    ExecutionEngine --> ResultProcessor[Result Processor]
    ResultProcessor --> Claude
    Claude --> User

Key Components:

  1. Skills Router: The central component that determines when to invoke skills and which skill to use based on user requests.

  2. Skill Registry: A catalog of available skills with their metadata, capabilities, and requirements.

  3. Execution Engine: Manages the execution lifecycle of skills, handling API calls, data transformation, and error management.

  4. Security Manager: Enforces authentication and authorization policies for skill usage.

  5. Result Processor: Transforms and formats the results from skills for integration into Claude’s responses.

Workflow of Claude Skills

The workflow of Claude Skills has been refined based on real-world usage patterns:

sequenceDiagram
    participant User
    participant Claude
    participant SkillsRouter
    participant Skill
    participant ExternalService
    
    User->>Claude: Makes a request
    Claude->>Claude: Analyzes request intent
    Claude->>SkillsRouter: Determines skill requirement
    SkillsRouter->>SkillsRouter: Validates permissions & context
    SkillsRouter->>Skill: Invokes skill with parameters
    Skill->>ExternalService: Makes API call
    ExternalService->>Skill: Returns data/result
    Skill->>Skill: Processes & validates result
    Skill->>SkillsRouter: Returns structured output
    SkillsRouter->>Claude: Provides processed result
    Claude->>Claude: Integrates result into response
    Claude->>User: Delivers final response

Workflow Steps:

  1. Intent Analysis: Claude analyzes the user’s request to determine the underlying intent and need.

  2. Skill Determination: The Skills Router identifies if a skill is needed and which specific skill would best address the request.

  3. Context Validation: The system verifies that the skill can be executed in the current context with appropriate permissions.

  4. Skill Invocation: The selected skill is called with the necessary parameters extracted from the user’s request.

  5. External Interaction: The skill interacts with external services or APIs to fulfill the request.

  6. Result Processing: The skill processes and validates the data received from external services.

  7. Response Integration: Claude incorporates the skill’s output into a coherent response.

  8. Delivery: The final response is presented to the user in a natural, conversational manner.

Creating Custom Skills

Creating a custom Claude Skill involves several key steps:

  1. Define the Skill’s Purpose: Clearly identify what task the skill will perform.

  2. Design the API Interface: Specify the endpoints, parameters, and authentication methods.

  3. Implement the Skill Logic: Develop the code that will execute when the skill is called.

  4. Test the Skill: Ensure the skill functions correctly in various scenarios.

  5. Deploy the Skill: Make the skill available for Claude to use.

  6. Configure Permissions: Set appropriate access controls for the skill.

Skill Definition Structure

Important: Skills are defined in Markdown format, not JSON. Each skill must have a SKILL.md file with YAML frontmatter.

Example SKILL.md structure:

---
name: example-skill
description: A skill that performs a specific task
version: 1.0.0
---

# Example Skill

This skill demonstrates how to create a custom Claude Skill.

## Purpose

Describe what this skill does and when it should be used.

## Tools

This skill can use the following tools:
- Tool 1: Description
- Tool 2: Description

## Workflow

1. Step 1: What the skill does first
2. Step 2: How it processes information
3. Step 3: How it returns results

## Usage Examples

Provide examples of how to use this skill.

## Additional Files

You can reference other Markdown files or Python scripts in this folder.

Note: SKILL.md can reference other Markdown files, making it highly flexible and composable.

Skills vs. MCP vs. A2A: Understanding the AI Agent Ecosystem

You might ask: since we could already accomplish these things with sub-agents or MCP, why invent the concept of Skills?

Context Window Management: The Key Difference

The main difference lies in how context windows are managed.

MCP’s Context Challenge: If you open one Claude instance and connect three MCP servers, just those servers can take up a big amount of the context window, because each connection loads all available tools, and the AI agent must decide which one to use at which step.

Sub-Agents’ Limitation: Sub-agents are more specialized, but their contexts are isolated from the main agent. When a sub-agent runs, it performs its steps and returns the result, but does not pass its intermediate reasoning back to the main agent.

Skills’ Advantage: Claude’s Skills work differently, and it all comes down to that SKILL.md file. It essentially contains the system prompt for that Skill, defining what it does, and includes any tools it can use.

Two Key Distinctions Between Skills and MCP

From a functional standpoint, Skills and MCP look similar (especially since the scripts act as tools), but there are two key distinctions:

Specialization and Workflow Definition: Each Skill is designed for repeatable workflows, you explicitly tell the AI how and in what order to use its tools. This makes Skills more deterministic and reliable for specific tasks.

Efficient Context Management (Progressive Disclosure): When multiple Skills exist, Claude only loads each Skill’s metadata (around 100–150 tokens) instead of full tool descriptions.

The Progressive Disclosure Process:

  1. When a user request arrives, Claude compares it semantically with the Skill descriptions
  2. Claude picks the most relevant Skills
  3. Only after selection does it load the full content of those Skills

Anthropic calls this design “Progressive Disclosure”, a principle that keeps Skills lightweight, flexible, and scalable.

Think of it as a well-organized manual:

  • Start with the table of contents
  • Open only the relevant chapter
  • Consult the appendix when necessary

Skills let Claude load only what’s needed, when it’s needed.

Functional Comparison: Skills vs. MCP

Aspect MCP Skills
Context Usage Token for all Tools Token per skill (metadata only)
Loading Strategy All tools loaded upfront Progressive disclosure
Workflow Definition Implicit, decided by AI Explicit, defined in SKILL.md
Context Sharing Full context available Selective loading
Specialization General-purpose tools Task-specific workflows
Model Support Model-agnostic protocol Claude-specific implementation

Relationship with MCP (Model Context Protocol)

  1. Standardization vs. Implementation:
    • MCP provides a protocol and framework for tool use across different LLMs.
    • Claude Skills represent a specific implementation of tool-using capabilities for Claude.
  2. Interoperability Potential:
    • Claude Skills could potentially adopt MCP standards for better interoperability.
    • MCP Server could include adapters for Claude Skills to make them available to other LLMs.
  3. MCP Server Cards as an Alternative:
    • MCP Server Cards provide functionality very similar to Claude Skills but in a model-agnostic way.
    • Cards can significantly reduce token consumption by packaging complex information in a structured format.
    • They enable consistent tool use patterns across different LLMs, not just Claude.
    • Organizations can implement a single tool integration strategy that works with multiple AI models.
  4. Complementary Approaches:
    • Rather than competitors, these technologies are better viewed as complementary approaches that address different aspects of the same challenge.
    • Organizations might use both: MCP for standardization across models and Claude Skills for Claude-specific capabilities.

Relationship with A2A (Adaptive AI Agent)

Claude Skills and A2A represent different approaches to extending AI capabilities:

  1. Architectural Differences:
    • Claude Skills are tightly integrated with Claude’s core system, allowing for seamless user experience.
    • A2A is designed as a more independent framework that can work with various LLMs, not just Claude.
  2. Integration Approach:
    • Claude Skills follow a more controlled, platform-specific approach managed by Anthropic.
    • A2A offers a more flexible, open architecture that can be customized and extended more freely.
  3. Complementary Strengths:
    • Claude Skills excel at providing specific, well-defined capabilities within Claude’s ecosystem.
    • A2A shines in scenarios requiring complex agent behaviors, multi-agent collaboration, and customizable workflows.
  4. Potential for Collaboration:
    • A2A could potentially leverage Claude Skills as specialized capabilities within its broader agent framework.
    • Claude could use A2A patterns for more complex, multi-step reasoning processes.

Best Practices for Claude Skills Development

  1. Focus on User Value: Design skills that solve real user problems rather than showcasing technical capabilities.

  2. Maintain Context Awareness: Ensure skills understand and preserve conversation context.

  3. Handle Errors Gracefully: Implement robust error handling and provide helpful feedback when things go wrong.

  4. Respect Privacy and Security: Be mindful of data handling practices and implement appropriate security measures.

  5. Design for Discoverability: Make skills easy to discover and understand for users.

  6. Test Thoroughly: Validate skills across a wide range of inputs and scenarios.

  7. Document Clearly: Provide comprehensive documentation for developers and users.

The Potential of Skills

It’s too early to say whether this model will become an industry standard, but its potential is enormous.

Industry Leadership

MCP was also proposed by Anthropic, and later adopted by many major model providers. Having the power to define such standards is the hallmark of a true industry leader.

Whether Skills will follow the same path depends on how widely the ecosystem embraces them.

Markdown as the Foundation

And yes, SKILL.md can even reference other Markdown files.

Markdown saves the day again!

This design choice makes Skills:

  • Easy to read and write
  • Version-control friendly
  • Composable and modular
  • Human and machine-readable

Future Directions

The Claude Skills ecosystem is likely to evolve in several directions:

  1. Increased Autonomy: Skills may gain more agency to perform complex tasks with less user guidance.

  2. Multi-Skill Orchestration: The ability to combine multiple skills to solve complex problems.

  3. Personalization: Skills that adapt to individual users’ preferences and needs.

  4. Collaborative Skills: Capabilities that facilitate collaboration between users and AI.

  5. Standardization: Potential adoption of cross-platform standards like those proposed by MCP.

  6. Ecosystem Growth: As more developers create Skills, we may see an “app store” model emerge for Claude Skills.

Conclusion

Claude Skills represent a significant advancement in making AI systems more capable and useful in real-world scenarios. By extending Claude’s core capabilities with specialized tools and external integrations, Skills transform Claude from a conversational AI into a more comprehensive assistant that can take actions and access information beyond its training data.

While Claude Skills, A2A agents, and MCP Server may appear to overlap in some areas, they are better understood as complementary technologies addressing different aspects of the same challenge: making AI systems more capable, flexible, and useful. Organizations looking to maximize the value of these technologies should consider how they can work together rather than viewing them as competing alternatives.

As the field continues to evolve, we can expect to see greater interoperability between these systems, with standards like those proposed by MCP potentially providing a common foundation for diverse implementations like Claude Skills and A2A agents.